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H&E-like histology of unstained fresh and formalin fixed breast tissue with Photo Acoustic Remote Sensing (PARS) microscopy.

2021· article· en· W3168252000 on OpenAlexaff
John R. Mackey, Benjamin R. Ecclestone, Deepak Dinakaran, Gilbert Bigras, Parsin Haji Reza

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPathologyH&E stainHistologyMedicineStainingMicroscopyFrozen section procedureBreast cancerCancer

Abstract

fetched live from OpenAlex

e12590 Background: Approximately 30% of breast cancer patients undergoing breast-conserving surgery (BCS) require re-excision(s) to obtain clear margins, causing delays in adjuvant treatment, poor aesthetic results, and increased infection rates, costs, and patient anxiety. Frozen section pathology assessment (FSA) reduces re-excision rates from 27% to 6%, but FSA extends operative times and has a false negative rate of 17%. Photo-Acoustic Remote Sensing (PARS) is a new laser-based light microscope that permits non-contact cellular resolution imaging of unstained tissues. Methods: We used an ultraviolet reflection-mode PARS microscope to study i) formalin-fixed, paraffin embedded (FFPE) breast tissue blocks, ii) unstained thin sections of FFPE invasive ductal carcinoma and ductal carcinoma in situ, iii) formalin fixed bulk surgical specimens of benign and malignant human breast tissue, and iv) benign and malignant fresh rodent mammary tissue without fixation, processing, or staining. Fresh tissues were imaged after a range of warm ischemic times and across a range of cold ischemic times in neutral phosphate buffered saline at 4°C. For each condition, an ultraviolet laser was used to excite DNA and other cellular components in these samples, and signals were captured using a continuous-wave detection laser. The PARS signal was false-colored to closely replicate traditional hematoxylin and eosin staining. Results: In all fixed tissues, PARS virtual histology images were of diagnostic quality, permitted margin assessment, and analogous to transmission light microscopy of standard hematoxylin and eosin-stained FFPE slides, achieved without tissue sectioning or tissue staining. Furthermore, PARS microscopy also provided cellular level virtual histology images in fresh breast tissue with warm and cold ischemic times ranging from twenty minutes to two hours; these images have no available clinical comparators as non-contact cellular level imaging of unprocessed fresh tissue has not previously been reported. In aggregate, we demonstrate the feasibility of PARS to provide diagnostic and margin assessment images across a range of tissues including formalin fixed and freshly resected, unstained, unprocessed breast tissue. Conclusions: PARS is a new microscope technology addressing the practical needs of intraoperative margin assessment during BCS: i) no requirement for tissue staining, ii) rapid acquisition of hematoxylin and eosin-like images without the requirement for tissue freezing, embedding, or sectioning, iii) diagnostic quality cellular resolution, and iv) assessment of resected bulk tissue margins. In principle, this technology may also permit label-free non-contact intraoperative margin assessment of the surgical cavity. These data support the clinical development and evaluation of PARS microscopic intraoperative assessment of BCS margins.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0140.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.399
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2021
Admission routes1
Has abstractyes

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